(1) Field of the Invention
The present invention generally relates to a neuron unit, and more particularly to a neuron unit which is modeled on a nervous cell and applied to neural computers.
(2) Description of Related Art
A neural network has been proposed which carries out a parallel processing of information. The neural network is modeled on functions of nervous cells which are units for processing information in a living human body, so that the neural network includes neuron units modeled on the nervous cells and connected to each other to form a network. It is relatively difficult for conventional Neumann computers to carry out a character recognition, an associative storage, a motion control and the like, while they are easily carried out in a living human body. The neural network is modeled on a nervous system in a living human body so that it is possible to realize parallel processing and a learning function which are characteristic of the nervous system in the living human body. Thus, the neural network can easily carry out character recognition, associative storage, motion control and the like. Functions in the neural network are, in general, realized by use of a computer simulation. However, it is preferable that the neural network be formed of hardware to perform parallel processing of the information.
Conventionally, Japanese Patent Laid Open Publication No.62-295188 discloses a neural network formed of hardware as shown in FIG. 1.
Referring to FIG. 1, a neural network includes a resistive feedback circuit network 3, CR circuits 2 connected to the resistive feedback circuit network 3 and amplifiers 1, each of which is connected to a corresponding one of the CR circuits 2. In this neural network, an intensity of coupling between the nervous cells is described by a resistance of a resistor T.sub.ij (a lattice point within the resistive feedback circuit network 3), and a nervous response function is described by an S-curve transfer function set in each of the amplifiers 1. The resistive feedback circuit network 3 feeds back an output of each of the amplifiers 1 to an input of each of the amplifiers 1 via a corresponding one of the CR circuits 2 as indicated by a one-dot chain line in FIG. 1. Input currents I.sub.1, I.sub.2, . . . , and I.sub.N are respectively applied to inputs of the amplifiers 1 via the CR circuits 2. In addition, the coupling between the nervous cells may be categorized as being either an excitation or an inhibition coupling, and such couplings are mathematically described by positive and negative signs of weighting coefficients. However, it is difficult to realize the positive and negative values by the circuit constants. Hence, the output of each of the amplifiers 1 is divided into two signals, and one of the two signals is inverted so as to generate a positive signal and a negative signal. An output of the neural network is derived from a collection of output voltages of the amplifiers 1.
FIG. 2 shows a modification of the neural network shown in FIG. 1. This modification is disclosed in the above Japanese Patent Laid Open Publication No.62-295188. In this case, the neural network is simplified based on a mathematical analysis. Negative gain amplifiers 4 each of which produces a single output are used in place of the amplifiers 1 shown in FIG. 1. The fundamental modification is similar to that of neural network shown in FIG. 1.
The conventional neural networks shown in FIGS. 1 and 2 are formed of analog circuits. In other words, the input and output quantities are described in current values or voltage values, and all operations within the circuits are carried out in analog form.
However, it is difficult to ensure accurate and stable operation of an analog circuit because the characteristics of the circuits forming the neural network change depending on the temperature, a drift occurs during an initial stage of the circuit operation when the power source is turned on, and the like. Particularly in the case of the neural network, at least several hundred amplifiers are required, and operation stability is critical since a non-linear operation is carried out.
In view of the above, a neural network formed of digital circuits is proposed by Hirai et al., in "Design of a Completely Digital Neuro-Chip", Technical Report of the Electronic Information and Communication Society, ICD88-130. But this digital neural network is simply an emulation of the conventional analog neural network, and the circuit construction of the digital neural network is quite complex in that up-down counters and the like are required.
To eliminate the above disadvantage of the conventional digital neural network, the applicant has proposed a digital neuron model in Japanese Patent Application No.1-179629. In this conventional digital neuron model, the relationship between the input and the output is fixedly determined. For example, when the input is "0", the output is always "0".